1. Field of the Invention
The present invention relates generally to complex network analysis, and particularly to a system and method for determining the feedback capacity of information distributed in a complex network as the information is received and diffused throughout the network.
2. Description of the Related Art
In the context of network theory, a complex network can be a graph (network) with non-trivial topological features, i.e., features that do not occur in simple networks, such as chains, grids, lattices, and fully-connected graphs often occur in real life situations. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of other real-world networks, such as computer networks and social networks. Examples of complex networks can include information networks, social networks, technological networks, or biological networks.
In social networks, the entities can be, for example, individuals, groups, or organizations. Examples of relationships among the entities in social networks can include communications, such as e-mail, telephone, or physical meetings. An example of a biological network is a metabolic network in which the entities are metabolic substrates, and the relationships are chemical reactions between the substrates. Examples of technological networks can include an electrical power grid (e.g., nodes are power plants, and edges are power lines), and a computer network as can include a local area network (LAN), a wide area network (WAN), cellular network, radio network, broadcasting network, intranet, extranet, the Internet (e.g., nodes are routers or machines, and edges are network connections), cloud network, etc. A complex network model can be used to identify a level of information that has been dispersed throughout a network as an indicator at which the information feedback is no longer significant in the network, i.e., the network feedback capacity.
The rapid and global emergence of complex networks over the past few years and their adoption by a large number of users, such as over the Internet, make complex networks now one of society's most powerful methods of rapidly spreading news and key information to large populations. Therefore, it is desirable to understand and analyze these complex networks in which data is transferred through associations and data flows in cycles in a manner that can ensure the information distributed is accurate, as well as to determine the information's value as it is distributed in the network.
Thus, a method and system for determining the feedback capacity of information distributed in a complex network addressing the aforementioned problems is desired.